Once more, with feeling! Using creative affective metaphors to express information needs

Abstract

Creative metaphors abound in language because they facilitate communication that is memorable, effective and elastic. Such metaphors allow a speaker to be maximally suggestive while being minimally committed to any single interpretation, so they can both supply and elicit information in a conversation. Yet, though metaphors are often used to articulate affective viewpoints and information needs in everyday language, they are rarely used in information retrieval (IR) queries. IR fails to distinguish between creative and uncreative uses of words, since it typically treats words as literal mentions rather than suggestive allusions. We show here how a computational model of affective comprehension and generation allows IR users to express their information needs with creative metaphors that concisely allude to a dense body of assertions. The key to this approach is a lexicon of stereotypical concepts and their affective properties. We show how such a lexicon is harvested from the open web and from local web n-grams.